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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
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RNA-seq03:21

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Related Experiment Video

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An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
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SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data.

Collin Giguere1, Harsh Vardhan Dubey1, Vishal Kumar Sarsani1

  • 1Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA.

BMC Bioinformatics
|May 28, 2020
PubMed
Summary

A new DNA sequencing data simulator handles nested samples from single-cell and bulk tissues. This tool aids developers in assessing the accuracy and precision of genomic analysis methods.

Keywords:
DNA sequencingHierarchical DirichletSingle-cell DNA sequencingsimulator

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Area of Science:

  • Genomics
  • Computational Biology

Background:

  • Next-generation DNA sequencing generates complex datasets from multiple biological samples.
  • Existing tools lack the capability to simulate nested sampling arrangements with both single-cell and bulk tissue data.
  • This limitation hinders the development and validation of accurate analysis methods.

Purpose of the Study:

  • To develop a novel tool for simulating DNA sequencing data.
  • To support nested sampling designs incorporating single-cell and bulk samples.
  • To facilitate the assessment of accuracy and precision in genomic analysis methods.

Main Methods:

  • Developed a simulation tool for DNA sequencing data.
  • The tool supports hierarchically grouped (correlated) samples.
  • Configuration files define experimental arrangements for simulation.

Main Results:

  • The simulator generates DNA sequencing data from nested single-cell and bulk samples.
  • The tool is configurable via a simple file.
  • Generated data is representative of real-world sequencing outputs.

Conclusions:

  • The developed DNA sequencing simulator produces realistic data.
  • The tool integrates smoothly with existing downstream analysis pipelines.
  • Enables robust testing of variant callers and other genomic software.